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Spycrop

2022

Github Link 1

Github Link 2

My Role

Fullstack Developer

Timeline

March 2022 - May 2022

Github

https://github.com/annuraggg/Spycrop-Web

https://github.com/annuraggg/SpyCrop-Desktop-App

Overview

Spycrop is a web application that utilizes computer vision to detect whether a person is wearing a mask. It uses machine learning models to analyze images and identify individuals without masks. If the application detects the absence of a mask, it triggers an alert mechanism, notifying the user and relevant authorities. SpyCrop is designed to be fast, accurate, and user-friendly, with a clean and intuitive interface.

The Problem

The COVID-19 pandemic has highlighted the importance of wearing masks in public spaces. However, monitoring mask compliance can be challenging, especially in high-traffic areas like airports, hospitals, and schools.

The Solution

Spycrop uses computer vision and machine learning to detect individuals without masks, helping organizations monitor mask compliance and enforce safety protocols. By providing real-time alerts and notifications, the application helps prevent the spread of COVID-19 and other infectious diseases. It also provides attendance tracking based on facial recognition to track attendance and mask compliance for employees, students, and visitors.

Technologies

Python

Flask

OpenCV

TensorFlow

HTML

CSS

JavaScript

Highlights

Mask Detection: Identify individuals without masks using computer vision and machine learning models.

Alert Mechanism: Trigger alerts and notifications when the application detects the absence of a mask.

Real-Time Monitoring: Monitor mask compliance in real time and generate reports for analysis.

Attendance Tracking: Track attendance and mask compliance for employees, students, and visitors by facial recognition.

Thank you